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Supervised breast cancer prediction using integrated dimensionality reduction convolutional neural network.

HuanQing Xu1, Xian Shao2,3, Shiji Hui4

  • 1The School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China.

Plos One
|May 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach using Conditional Deep Convolution Generative Adversarial Networks (CDCGAN) and an integrated dimension reduction convolutional neural network (IDRCNN) for accurate breast cancer classification, improving early detection and treatment outcomes.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer poses a significant health challenge with high mortality rates.
  • Early detection is crucial for effective treatment and improved patient outcomes.
  • Accurate classification of benign versus malignant tumors is essential.

Purpose of the Study:

  • To introduce a novel deep learning-based method for breast cancer classification.
  • To address data imbalance and high-dimensionality challenges in breast cancer datasets.
  • To enhance the accuracy of computer-aided detection (CAD) systems for breast tumors.

Main Methods:

  • Utilized Conditional Deep Convolution Generative Adversarial Network (CDCGAN) to generate synthetic data, mitigating issues with unbalanced pathological tumor samples.
  • Developed an integrated dimension reduction convolutional neural network (IDRCNN) model to handle high-dimensional data and extract relevant features from breast cancer samples.
  • Integrated CDCGAN with IDRCNN for a comprehensive CAD system to classify breast tumor cell samples.

Main Results:

  • The proposed IDRCNN model demonstrated improved accuracy in classifying breast cancer.
  • The combined CDCGAN and IDRCNN models achieved superior classification performance compared to existing methods.
  • Performance was validated through comprehensive metrics including sensitivity, AUC, ROC curve, accuracy, recall, specificity, precision, PPV, NPV, and F-values.

Conclusions:

  • Conditional Deep Convolution Generative Adversarial Network (CDCGAN) effectively addresses data imbalance by generating targeted small sample datasets.
  • The integrated dimension reduction convolutional neural network (IDRCNN) model successfully reduces data dimensionality and extracts key features in breast cancer analysis.
  • The synergistic application of CDCGAN and IDRCNN offers a powerful tool for accurate breast cancer classification, advancing diagnostic capabilities.